1. 1 Introduction to biostatistics By Dr. S. Shaffi Ahamed Asst. Professor Dept. of Family & Community Medicine KKUH

2. 2 This session covers: Background and need to know Biostatistics Definition of Statistics and Biostatistics Types of data Frequency distribution of a data Graphical representation of a data

3. 3 Statistics is the science of conducting studies to collect, organize, summarize, analyze, present, interpret and draw conclusions from data. Any values (observations or measurements) that have been collected

4. 4 What Is Statistics? Why? 1. Collecting Data e.g., Sample, Survey, Observe, Simulate 2. Characterizing Data e.g., Organize/Classify, Count, Summarize 3. Presenting Data e.g., Tables, Charts, Statements 4. Interpreting Results e.g. Infer, Conclude, Specify Confidence Data Analysis Decision- Making © 1984-1994 T/Maker Co.

5. 5 “BIOSTATISICS ” (1) Statistics arising out of biological sciences, particularly from the fields of Medicine and public health. (2) The methods used in dealing with statistics in the fields of medicine, biology and public health for planning, conducting and analyzing data which arise in investigations of these branches.

6. 6 CLINICAL MEDICINE Documentation of medical history of diseases. Planning and conduct of clinical studies. Evaluating the merits of different procedures. In providing methods for definition of “normal” and “abnormal”.

7. 7 PREVENTIVE MEDICINE To provide the magnitude of any health problem in the community. To find out the basic factors underlying the ill-health. To evaluate the health programs which was introduced in the community (success/failure). To introduce and promote health legislation.

8. 8 Role of Biostatics in Health Planning and Evaluation In carrying out a valid and reliable health situation analysis, including in proper summarization and interpretation of data. In proper evaluation of the achievements and failures of a health programs.

9. 9 Role of Biostatistics in Medical Research In developing a research design that can minimize the impact of uncertainties In assessing reliability and validity of tools and instruments to collect the information In proper analysis of data

10. What can we do with Biostatistics? Summarize data Generalize from a sample to a population Compare groups Test for relationships Analyze multiple variables and their relationship to a given outcome

11. What can we do with Biostatistics? Assess time to an event as an endpoint Report the characteristics of a test Combine results of several studies Assess costs and consequences of treatment Report decision analyses and clinical practice guidelines

12. 12 BASIC CONCEPTS Data : Set of values of one or more variables recorded on one or more observational units (singular: Datum) Categories of data 1. Primary data: observation, questionnaire, record form, interviews, survey, 2. Secondary data: census, medical record,registry Sources of data 1. Routinely kept records 2. Surveys (census) 3. Experiments 4. External source

13. 13 Variables and Types of Data Variables and Types of Data To gain knowledge about seemingly haphazard events, statisticians collect information for variables, which describe the event. Variables whose values are determined by chance are called random variables Variables is a characteristic or attribute that can assume different values. is also a characteristics of interest, one that can be expressed as a number that possessed by each item under study. The value of this characteristics is likely to change or vary from one item in the data set to the next.

14. 14 Variables can be classified By how they are categorized, counted or measured - Level of measurements of data As Quantitative and Qualitative

15. 15 Nomenclature Nominal variable: Variable consists of named categories with no implied order among them. Has cancer or not Received treatment or did not Is alive or dead Is coded (male = 1, female = 2) but has no quantitative value.

16. 16 Nomenclature (cont.) Ordinal variable: Variable consists of ordered categories and differences between categories are not equal. Patient status (Improved / Same / Worse) Diagnosis (Stage I / Stage II / Stage III) Evaluation (Satisfied / neutral / Dissatisfied) The coding now has meaning: Improved = 2, Same = 1, worse = 0 However, distant between values is not a constant.

17. 17 Other Nomenclature (cont.) Interval variable: Variable has equal distances between values but the zero point is arbitrary. IQ 70 to 80 same as IQ 90 to 100. IQ scale could convert 100 to 500 and have same meaning. Differences between numbers are meaningful but the ratios between them are not. IQ of 100 is not twice as smart as IQ of 50.

18. 18 Other Nomenclature (cont.) Ratio variable: Variable has equal intervals between values and a meaningful zero point. Height, Weight 220 pounds is twice as heavy as 110 pounds. Even when converted to kilos, the ratio stays the same (100 kilos is twice as heavy as 50 kilos).

19. 19 Scales of Measurement QualitativeQualitativeQuantitativeQuantitative NumericalNumerical NumericalNumerical NonnumericalNonnumerical DataData NominalNominalOrdinalOrdinalNominalNominalOrdinalOrdinalIntervalIntervalRatioRatio

20. 20 Level of Measurements of Data Examples

21. 21 Discrete data -- Gaps between possible values Continuous data -- Theoretically, no gaps between possible values Number of Children Hb

22. 22 CONTINUOUS DATA QUALITATIVE DATA wt. (in Kg.) : under wt, normal & over wt. Ht. (in cm.): short, medium & tall

23. 23 Table 1 Distribution of blunt injured patients according to hospital length of stay

24. 24 CLINIMETRICS A science called clinimetrics in which qualities are converted to meaningful quantities by using the scoring system. Examples: (1) Apgar score based on appearance, pulse, grimace, activity and respiration is used for neonatal prognosis. (2) Smoking Index: no. of cigarettes, duration, filter or not, whether pipe, cigar etc., (3) APACHE( Acute Physiology and Chronic Health Evaluation) score: to quantify the severity of condition of a patient

25. 25 INVESTIGATION

26. 26 Frequency Distributions “ A Picture is Worth a Thousand Words ”

27. 27 Frequency Distributions data distribution – pattern of variability. the center of a distribution the ranges the shapes simple frequency distributions grouped frequency distributions

28. 28 Simple Frequency Distribution The number of times that score occurs Make a table with highest score at top and decreasing for every possible whole number N (total number of scores) always equals the sum of the frequency  f = N

29. 29 Example of a simple frequency distribution 5 7 8 1 5 9 3 4 2 2 3 4 9 7 1 4 5 6 8 9 4 3 5 2 1 f 93 82 72 61 54 44 33 23 13  f = 25

30. 30 Relative Frequency Distribution Proportion of the total N Divide the frequency of each score by N Rel. f = f/N Sum of relative frequencies should equal 1.0 Gives us a frame of reference

31. 31 Example of a simple frequency distribution 5 7 8 1 5 9 3 4 2 2 3 4 9 7 1 4 5 6 8 9 4 3 5 2 1 f rel f 93.12 82.08 72.08 61.04 54.16 44.16 33.12 23.12 13.12  f = 25  rel f = 1.0

32. 32 Cumulative Frequency Distributions cf = cumulative frequency: number of scores at or below a particular score A score’s standing relative to other scores Count from lower scores and add the simple frequencies for all scores below that score

33. 33 Example of a simple frequency distribution 5 7 8 1 5 9 3 4 2 2 3 4 9 7 1 4 5 6 8 9 4 3 5 2 1 f rel f cf 93.12 3 82.08 5 72.08 7 61.04 8 54.16 12 44.16 16 33.12 19 23.12 22 13.12 25  f = 25  rel f = 1.0

34. 34 Patien t No Hb (g/dl) Patien t No Hb (g/dl) Patien t No Hb (g/dl) 112.01111.22114.9 211.91213.62212.2 311.51310.82312.2 414.21412.32411.4 512.31512.32510.7 613.01615.72612.5 710.51712.62711.8 812.8189.12815.1 913.21912.92913.4 1011.22014.63013.1 Tabulate the hemoglobin values of 30 adult male patients listed below

35. 35 Steps for making a table Step1 Find Minimum (9.1) & Maximum (15.7) Step2 Calculate difference 15.7 – 9.1 = 6.6 Step3 Decide the number and width of the classes (7 c.l) 9.0 -9.9, 10.0-10.9,---- Step4 Prepare dummy table – Hb (g/dl), Tally mark, No. patients

36. 36 Hb (g/dl)Tall marksNo. patients 9.0 – 9.9 10.0 – 10.9 11.0 – 11.9 12.0 – 12.9 13.0 – 13.9 14.0 – 14.9 15.0 – 15.9 Total Hb (g/dl) Tall marksNo. patients 9.0 – 9.9 10.0 – 10.9 11.0 – 11.9 12.0 – 12.9 13.0 – 13.9 14.0 – 14.9 15.0 – 15.9 l lll llll 1 llll lll ll 1 3 6 10 5 3 2 Total-30 DUMMY TABLE Tall Marks TABLE

37. 37 Hb (g/dl)No. of patients 9.0 – 9.9 10.0 – 10.9 11.0 – 11.9 12.0 – 12.9 13.0 – 13.9 14.0 – 14.9 15.0 – 15.9 1 3 6 10 5 3 2 Total30 Table Frequency distribution of 30 adult male patients by Hb

38. 38 Table Frequency distribution of adult patients by Hb and gender: Hb and gender: Hb (g/dl) GenderTotal MaleFemale <9.0 9.0 – 9.9 10.0 – 10.9 11.0 – 11.9 12.0 – 12.9 13.0 – 13.9 14.0 – 14.9 15.0 – 15.9 0 1 3 6 10 5 3 2 2358642023586420 4 8 14 16 9 5 2 Total30 60

39. 39 Elements of a Table Ideal table should have Number Title Column headings Foot-notes Number – Table number for identification in a report Title,place - Describe the body of the table, variables, Time period (What, how classified, where and when) Column - Variable name, No., Percentages (%), etc., Heading Foot-note(s) - to describe some column/row headings, special cells, source, etc.,

40. 40 DIAGRAMS/GRAPHS Qualitative data (Nominal & Ordinal) --- Bar charts (one or two groups) Quantitative data (discrete & continuous) --- Histogram --- Frequency polygon (curve) --- Stem-and –leaf plot --- Box-and-whisker plot

41. 41 Example data 6863422730362832 7927222824254465 4325745136422831 2825451257511232 4938422731503821 1624644723224327 4928231911524631 30434912

42. 42 Histogram Figure 1 Histogram of ages of 60 subjects

43. 43 Polygon

44. 44 Cumulative Frequency Polygon Cumulative counts can be converted to percents. Shows number cases up to & including all within the interval. % # Common in vital statistics 50 30

45. 45 Example data 6863422730362832 7927222824254465 4325745136422831 2825451257511232 4938422731503821 1624644723224327 4928231911524631 30434912

46. 46 Stem and leaf plot Stem-and-leaf of Age N = 60 Leaf Unit = 1.0 6 1 122269 19 2 1223344555777788888 (11) 3 00111226688 13 4 2223334567999 5 5 01127 4 6 3458 2 7 49

47. 47 Box plot

48. 48 Descriptive statistics report: Boxplot - minimum score - maximum score - lower quartile - upper quartile - median - mean - the skew of the distribution: positive skew: mean > median & high-score whisker is longer negative skew: mean < median & low-score whisker is longer

49. 49 Application of a box and Whisker diagram

50. 50 The prevalence of different degree of Hypertension in the population Pie Chart Circular diagram – total -100% Divided into segments each representing a category Decide adjacent category The amount for each category is proportional to slice of the pie

51. 51 Bar Graphs The distribution of risk factor among cases with Cardio vascular Diseases Heights of the bar indicates frequency Frequency in the Y axis and categories of variable in the X axis The bars should be of equal width and no touching the other bars

52. 52 HIV cases enrolment in USA by gender Bar chart

53. 53 HIV cases Enrollment in USA by gender Stocked bar chart

54. 54 Graphic Presentation of Data the histogram (quantitative data) the bar graph (qualitative data) the frequency polygon (quantitative data)

55. 55

56. 56 General rules for designing graphs A graph should have a self-explanatory legend A graph should help reader to understand data Axis labeled, units of measurement indicated Scales important. Start with zero (otherwise // break) Avoid graphs with three-dimensional impression, it may be misleading (reader visualize less easily

57. 57 Any Questions